Most hypergrowth startups hire fast and regret it later.

They compromise on quality to fill seats, rationalize cultural misfits as “learning experiences,” and spend months fixing the technical debt created by rushed hires. Adam Kirk took a different approach: he hired 35 engineers in 12 months and raised his quality bar with each hire.

Adam is co-founder and CTO of Jump, where they’ve built a note-taking platform specifically for financial advisors that grew from 4 people to 50 in one year. But here’s what makes his story different: instead of the typical startup hiring playbook, he invented a process that lets him see how candidates actually work before making decisions.

The result? A team scaling at breakneck speed without the usual quality compromises. Here’s how he did it—and why traditional hiring advice fails when you’re moving this fast.

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The Extreme Ownership Filter

Traditional hiring advice says “hire for potential and train for skills.” Adam learned this doesn’t work when you’re drowning in growth and can’t afford hand-holding.

“As a matter of survival, I’m looking for somebody who can just, you know, I can say like, look, take this part of the product and own it, and I don’t make it so that I don’t have to think about it anymore,” he explained. “Make excellent decisions, build it really fast. Fix all the bugs. Deliver a ton of value to customers such that I don’t really have to think about it anymore.”

This isn’t just preference—it’s survival. When you’re making 100 decisions per day and reading hundreds of Slack messages, you need people who can take complete ownership without ongoing guidance.

The contrast with most companies is stark: “One feedback I got from somebody was that most companies want engineers to constantly be asking for permission or getting validation on what they’re doing. The way that we work is sort of like, no, I trust you. Just go get it done.”

This filter eliminates 90% of candidates immediately. But for hypergrowth companies, hiring someone who needs constant direction is actually more expensive than not hiring at all.

The Trial Week Revolution

Here’s where Adam’s approach gets innovative: instead of trying to predict performance through interviews, he pays candidates to work for a week on real problems.

“We give them an actual real difficult challenge, like a ticket that we need built. We get to see them actually working on our code base, actually building something that we need,” he said.

The process starts with a 30-minute coding exercise to screen basic proficiency, then moves directly to a paid trial week. No multi-round interviews, no theoretical problems, no whiteboarding sessions.

What this reveals that traditional interviews can’t:

* How they handle ambiguity when requirements aren’t perfectly defined

* How they communicate when they’re stuck

* How they integrate with the existing team and codebase

* Whether they can actually deliver results in your specific environment

“Usually by halfway through the week, we know that this is somebody that we want to work with,” Adam noted. The approach is intensive—”you’re onboarding them, getting them set up, you’re evaluating their work constantly”—but it dramatically reduces hiring mistakes.

Most importantly, it works for candidates too: “It’s good for applicants because they get to see how’s the team? Do they like the code? Do they like the tech stack? Working with us for a week, they’re pretty sure whether or not it’s a good fit for them.”

When the CTO Can’t Scale

Adam’s honest about the personal cost of hypergrowth: the system that got them here isn’t sustainable for him personally.

“I make too many decisions every day. I have too much context switching fatigue. I’m reading hundreds of messages in Slack every day. I am being asked to make a hundred decisions every day,” he told me. “I wouldn’t describe my state as sustainable right now.”

The challenge isn’t just volume—it’s that the hardest problems naturally filter up: “All the crap kind of filters to the exec, up to the CEO or to the top. All engineering problems, the worst problems filter to me. And a lot of them are stuff that are not fun to deal with.”

But here’s his insight: there are only two “glass balls” he can’t drop that will compound over time—code quality and hiring quality. Everything else can be managed or delegated, but these two mistakes get more expensive every day you don’t fix them.

His solution is to hire people who can completely remove decision-making burden: “I need you to take one of these bricks that I’m holding and take it completely from me.”

The AI Productivity Reality Check

While the industry debates whether AI will replace engineers, Adam has measured actual results in a hypergrowth environment where productivity matters immediately.

“For some things it makes you 10 times faster, like writing tests. AI is so good at writing tests you should never write your own tests anymore,” he said. “Maybe it five Xs you while you’re typing out the code, maybe it two Xs you answering questions about the codebase.”

His measured assessment: “The effective increase over all those things combined is probably around 1.5 to 2x more productive.”

But here’s the strategic insight most leaders miss: this doesn’t reduce hiring needs. “If you take your 10 engineers, give them AI, and now they’re 20, your competitors are gonna hire 20 and double it to 40. You can’t hire less engineers.”

The competitive advantage isn’t using AI to hire fewer people—it’s using AI to make your existing team more capable while continuing to hire aggressively. Adam’s team uses AI extensively during trial weeks to see how candidates leverage these tools in real work.

What This Means for Your Startup

First, design your hiring process around actual work, not theoretical scenarios. If you can’t afford to pay someone for a week of real work, you probably can’t afford to hire them full-time either.

Second, hire for extreme ownership when scaling fast. Hand-holding kills velocity when you’re trying to move at hypergrowth speeds. Look for people who can take complete ownership of product areas without ongoing management overhead.

Third, accept that hypergrowth means accepting some unsustainability in exchange for speed. The question isn’t whether you’ll be overwhelmed—it’s whether you’re building systems that will eventually let you delegate the right decisions.

Fourth, use AI to amplify your best people, but don’t expect it to replace hiring. The 1.5-2x productivity gains are real, but your competitors will use the same tools. The advantage goes to whoever can hire and scale the fastest while maintaining quality.

The trial week test: Before your next hire, ask yourself: “Would I be comfortable paying this person for a week to work on a real problem?” If not, keep looking. The cost of a failed trial week is much less than the cost of a bad hire.

This conversation with Adam Kirk originally appeared on the High Output podcast. For more from-the-trenches insights from engineering leaders navigating hypergrowth and AI transformation, subscribe here.

High Output is brought to you by Maestro AI. Adam talked about drowning in “hundreds of Slack messages every day” and making “a hundred decisions every day”—but that decision fatigue creates a visibility problem that most engineering leaders face. When your team is distributed across Slack, Jira, and GitHub, it becomes impossible to see who’s actually delivering and where bottlenecks are forming. Maestro cuts through that chaos with daily briefings that reveal where your team’s time and energy actually go, so you can spot the high performers worth promoting and the blockers slowing everyone down.

Visit https://getmaestro.ai to see how we help engineering leaders make better decisions about their teams and projects.

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